Overview

Dataset statistics

Number of variables20
Number of observations1163
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory181.8 KiB
Average record size in memory160.1 B

Variable types

Boolean1
Text2
Categorical5
Numeric12

Alerts

Year has constant value ""Constant
Goal is highly skewed (γ1 = 28.63807862)Skewed
Min Pledge Tiers is highly skewed (γ1 = 22.66496847)Skewed
URL has unique valuesUnique
Title has unique valuesUnique
Backed Projects has 697 (59.9%) zerosZeros
Previous Projects has 902 (77.6%) zerosZeros
Images has 456 (39.2%) zerosZeros
Videos has 401 (34.5%) zerosZeros

Reproduction

Analysis started2024-04-14 16:53:44.236284
Analysis finished2024-04-14 16:54:00.736252
Duration16.5 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Funded
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
685 
True
478 
ValueCountFrequency (%)
False 685
58.9%
True 478
41.1%
2024-04-14T13:54:00.795305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

URL
Text

UNIQUE 

Distinct1163
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:01.035523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length105
Median length88
Mean length79.439381
Min length48

Characters and Unicode

Total characters92388
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1163 ?
Unique (%)100.0%

Sample

1st rowhttps://www.kickstarter.com/projects/mischaa/pixelstart-choose-your-own-pixels
2nd rowhttps://www.kickstarter.com/projects/1055874454/smart-shop-icons
3rd rowhttps://www.kickstarter.com/projects/minimalprints/minimal-haus-prints-digital-prints-for-diy-wall-ar
4th rowhttps://www.kickstarter.com/projects/797661619/neon-altering-the-alternative
5th rowhttps://www.kickstarter.com/projects/1983693599/nintendo-nes-8bit-retro-canvas
ValueCountFrequency (%)
https://www.kickstarter.com/projects/mischaa/pixelstart-choose-your-own-pixels 1
 
0.1%
https://www.kickstarter.com/projects/1696119948/brighton-fine-art-painting-degree-show 1
 
0.1%
https://www.kickstarter.com/projects/797661619/neon-altering-the-alternative 1
 
0.1%
https://www.kickstarter.com/projects/1983693599/nintendo-nes-8bit-retro-canvas 1
 
0.1%
https://www.kickstarter.com/projects/463662725/day-and-night-cards 1
 
0.1%
https://www.kickstarter.com/projects/jforwoodart/fund-an-art-show-and-beyond-for-jeff-forwood 1
 
0.1%
https://www.kickstarter.com/projects/109608826/trump-that-t-are-hilarious-creative-and-beautiful 1
 
0.1%
https://www.kickstarter.com/projects/299469641/pens-and-pedals 1
 
0.1%
https://www.kickstarter.com/projects/cowgirlem/once-upon-a-picture 1
 
0.1%
https://www.kickstarter.com/projects/1108038598/kokoro 1
 
0.1%
Other values (1153) 1153
99.1%
2024-04-14T13:54:01.620054image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 8415
 
9.1%
e 6115
 
6.6%
r 6021
 
6.5%
/ 5815
 
6.3%
s 5626
 
6.1%
- 4946
 
5.4%
o 4925
 
5.3%
c 4793
 
5.2%
a 4178
 
4.5%
w 3991
 
4.3%
Other values (30) 37563
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 8415
 
9.1%
e 6115
 
6.6%
r 6021
 
6.5%
/ 5815
 
6.3%
s 5626
 
6.1%
- 4946
 
5.4%
o 4925
 
5.3%
c 4793
 
5.2%
a 4178
 
4.5%
w 3991
 
4.3%
Other values (30) 37563
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 8415
 
9.1%
e 6115
 
6.6%
r 6021
 
6.5%
/ 5815
 
6.3%
s 5626
 
6.1%
- 4946
 
5.4%
o 4925
 
5.3%
c 4793
 
5.2%
a 4178
 
4.5%
w 3991
 
4.3%
Other values (30) 37563
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 8415
 
9.1%
e 6115
 
6.6%
r 6021
 
6.5%
/ 5815
 
6.3%
s 5626
 
6.1%
- 4946
 
5.4%
o 4925
 
5.3%
c 4793
 
5.2%
a 4178
 
4.5%
w 3991
 
4.3%
Other values (30) 37563
40.7%

Title
Text

UNIQUE 

Distinct1163
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:01.926331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length80
Median length52
Mean length35.918315
Min length1

Characters and Unicode

Total characters41773
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1163 ?
Unique (%)100.0%

Sample

1st rowPixelstart: Choose Your Own Pixels (Canceled)
2nd rowSmart shop Icons (Canceled)
3rd rowMinimal Haus Prints: Digital Prints for DIY Wall ART
4th rowNeoN: Altering the Alternative (Canceled)
5th rowNintendo NES 8bit retro canvas (Canceled)
ValueCountFrequency (%)
318
 
4.8%
the 259
 
3.9%
a 145
 
2.2%
for 114
 
1.7%
canceled 88
 
1.3%
and 69
 
1.0%
of 69
 
1.0%
to 64
 
1.0%
your 57
 
0.9%
app 54
 
0.8%
Other values (3167) 5373
81.3%
2024-04-14T13:54:02.421781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5445
 
13.0%
e 3616
 
8.7%
a 2406
 
5.8%
o 2308
 
5.5%
r 2192
 
5.2%
i 2184
 
5.2%
t 2112
 
5.1%
n 1988
 
4.8%
s 1505
 
3.6%
l 1476
 
3.5%
Other values (77) 16541
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5445
 
13.0%
e 3616
 
8.7%
a 2406
 
5.8%
o 2308
 
5.5%
r 2192
 
5.2%
i 2184
 
5.2%
t 2112
 
5.1%
n 1988
 
4.8%
s 1505
 
3.6%
l 1476
 
3.5%
Other values (77) 16541
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5445
 
13.0%
e 3616
 
8.7%
a 2406
 
5.8%
o 2308
 
5.5%
r 2192
 
5.2%
i 2184
 
5.2%
t 2112
 
5.1%
n 1988
 
4.8%
s 1505
 
3.6%
l 1476
 
3.5%
Other values (77) 16541
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5445
 
13.0%
e 3616
 
8.7%
a 2406
 
5.8%
o 2308
 
5.5%
r 2192
 
5.2%
i 2184
 
5.2%
t 2112
 
5.1%
n 1988
 
4.8%
s 1505
 
3.6%
l 1476
 
3.5%
Other values (77) 16541
39.6%

Year
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2016
1163 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4652
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 1163
100.0%

Length

2024-04-14T13:54:02.572918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:54:02.670006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2016 1163
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1163
25.0%
0 1163
25.0%
1 1163
25.0%
6 1163
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1163
25.0%
0 1163
25.0%
1 1163
25.0%
6 1163
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1163
25.0%
0 1163
25.0%
1 1163
25.0%
6 1163
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1163
25.0%
0 1163
25.0%
1 1163
25.0%
6 1163
25.0%

Month
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
Mar
823 
Apr
185 
Feb
151 
May
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3489
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr
2nd rowApr
3rd rowApr
4th rowMar
5th rowMar

Common Values

ValueCountFrequency (%)
Mar 823
70.8%
Apr 185
 
15.9%
Feb 151
 
13.0%
May 4
 
0.3%

Length

2024-04-14T13:54:02.777104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:54:02.886203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
mar 823
70.8%
apr 185
 
15.9%
feb 151
 
13.0%
may 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 1008
28.9%
M 827
23.7%
a 827
23.7%
A 185
 
5.3%
p 185
 
5.3%
F 151
 
4.3%
e 151
 
4.3%
b 151
 
4.3%
y 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1008
28.9%
M 827
23.7%
a 827
23.7%
A 185
 
5.3%
p 185
 
5.3%
F 151
 
4.3%
e 151
 
4.3%
b 151
 
4.3%
y 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1008
28.9%
M 827
23.7%
a 827
23.7%
A 185
 
5.3%
p 185
 
5.3%
F 151
 
4.3%
e 151
 
4.3%
b 151
 
4.3%
y 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1008
28.9%
M 827
23.7%
a 827
23.7%
A 185
 
5.3%
p 185
 
5.3%
F 151
 
4.3%
e 151
 
4.3%
b 151
 
4.3%
y 4
 
0.1%

Type
Categorical

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
Video
244 
Design
197 
Crafts
172 
Apps
172 
Software
149 
Other values (2)
229 

Length

Max length8
Median length7
Mean length5.4729149
Min length3

Characters and Unicode

Total characters6365
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArt
2nd rowArt
3rd rowArt
4th rowArt
5th rowArt

Common Values

ValueCountFrequency (%)
Video 244
21.0%
Design 197
16.9%
Crafts 172
14.8%
Apps 172
14.8%
Software 149
12.8%
Art 138
11.9%
Gadgets 91
 
7.8%

Length

2024-04-14T13:54:03.016320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:54:03.143435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
video 244
21.0%
design 197
16.9%
crafts 172
14.8%
apps 172
14.8%
software 149
12.8%
art 138
11.9%
gadgets 91
 
7.8%

Most occurring characters

ValueCountFrequency (%)
e 681
 
10.7%
s 632
 
9.9%
t 550
 
8.6%
r 459
 
7.2%
i 441
 
6.9%
a 412
 
6.5%
o 393
 
6.2%
p 344
 
5.4%
d 335
 
5.3%
f 321
 
5.0%
Other values (9) 1797
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 681
 
10.7%
s 632
 
9.9%
t 550
 
8.6%
r 459
 
7.2%
i 441
 
6.9%
a 412
 
6.5%
o 393
 
6.2%
p 344
 
5.4%
d 335
 
5.3%
f 321
 
5.0%
Other values (9) 1797
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 681
 
10.7%
s 632
 
9.9%
t 550
 
8.6%
r 459
 
7.2%
i 441
 
6.9%
a 412
 
6.5%
o 393
 
6.2%
p 344
 
5.4%
d 335
 
5.3%
f 321
 
5.0%
Other values (9) 1797
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 681
 
10.7%
s 632
 
9.9%
t 550
 
8.6%
r 459
 
7.2%
i 441
 
6.9%
a 412
 
6.5%
o 393
 
6.2%
p 344
 
5.4%
d 335
 
5.3%
f 321
 
5.0%
Other values (9) 1797
28.2%

Has FB
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
1
599 
0
564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1163
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 599
51.5%
0 564
48.5%

Length

2024-04-14T13:54:03.286566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:54:03.387657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 599
51.5%
0 564
48.5%

Most occurring characters

ValueCountFrequency (%)
1 599
51.5%
0 564
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 599
51.5%
0 564
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 599
51.5%
0 564
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 599
51.5%
0 564
48.5%

Backed Projects
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9982803
Minimum0
Maximum348
Zeros697
Zeros (%)59.9%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:03.510769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile18
Maximum348
Range348
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.350523
Coefficient of variation (CV)3.8392813
Kurtosis236.42687
Mean3.9982803
Median Absolute Deviation (MAD)0
Skewness12.63085
Sum4650
Variance235.63855
MonotonicityNot monotonic
2024-04-14T13:54:03.668912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 697
59.9%
1 127
 
10.9%
2 62
 
5.3%
3 46
 
4.0%
5 32
 
2.8%
4 27
 
2.3%
6 21
 
1.8%
8 14
 
1.2%
11 12
 
1.0%
7 11
 
0.9%
Other values (44) 114
 
9.8%
ValueCountFrequency (%)
0 697
59.9%
1 127
 
10.9%
2 62
 
5.3%
3 46
 
4.0%
4 27
 
2.3%
5 32
 
2.8%
6 21
 
1.8%
7 11
 
0.9%
8 14
 
1.2%
9 8
 
0.7%
ValueCountFrequency (%)
348 1
0.1%
151 1
0.1%
143 1
0.1%
107 2
0.2%
104 1
0.1%
99 1
0.1%
79 1
0.1%
70 1
0.1%
57 2
0.2%
55 1
0.1%

Previous Projects
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8194325
Minimum0
Maximum29
Zeros902
Zeros (%)77.6%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:03.808038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4187451
Coefficient of variation (CV)2.9517319
Kurtosis55.034518
Mean0.8194325
Median Absolute Deviation (MAD)0
Skewness6.3879544
Sum953
Variance5.8503277
MonotonicityNot monotonic
2024-04-14T13:54:03.930150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 902
77.6%
2 156
 
13.4%
3 44
 
3.8%
4 22
 
1.9%
6 7
 
0.6%
5 7
 
0.6%
8 4
 
0.3%
9 4
 
0.3%
10 2
 
0.2%
29 2
 
0.2%
Other values (11) 13
 
1.1%
ValueCountFrequency (%)
0 902
77.6%
2 156
 
13.4%
3 44
 
3.8%
4 22
 
1.9%
5 7
 
0.6%
6 7
 
0.6%
7 1
 
0.1%
8 4
 
0.3%
9 4
 
0.3%
10 2
 
0.2%
ValueCountFrequency (%)
29 2
0.2%
24 1
0.1%
23 1
0.1%
20 1
0.1%
18 1
0.1%
17 1
0.1%
16 1
0.1%
14 1
0.1%
13 1
0.1%
12 2
0.2%

Creator Desc Len
Real number (ℝ)

Distinct468
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.89854
Minimum0
Maximum757
Zeros9
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:04.076281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile62.1
Q1221
median403
Q3504
95-th percentile538
Maximum757
Range757
Interquartile range (IQR)283

Descriptive statistics

Standard deviation167.45493
Coefficient of variation (CV)0.46399449
Kurtosis-1.0111924
Mean360.89854
Median Absolute Deviation (MAD)110
Skewness-0.46850716
Sum419725
Variance28041.155
MonotonicityNot monotonic
2024-04-14T13:54:04.261450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 33
 
2.8%
499 31
 
2.7%
498 28
 
2.4%
507 19
 
1.6%
506 18
 
1.5%
505 17
 
1.5%
511 16
 
1.4%
503 16
 
1.4%
504 15
 
1.3%
514 15
 
1.3%
Other values (458) 955
82.1%
ValueCountFrequency (%)
0 9
0.8%
3 1
 
0.1%
6 1
 
0.1%
9 1
 
0.1%
10 2
 
0.2%
12 1
 
0.1%
13 1
 
0.1%
18 1
 
0.1%
19 1
 
0.1%
21 1
 
0.1%
ValueCountFrequency (%)
757 1
0.1%
747 1
0.1%
705 1
0.1%
679 1
0.1%
677 1
0.1%
670 2
0.2%
668 1
0.1%
667 1
0.1%
661 1
0.1%
660 1
0.1%

Title Len
Real number (ℝ)

Distinct73
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.725709
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:04.419593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q121
median37
Q353
95-th percentile61
Maximum82
Range81
Interquartile range (IQR)32

Descriptive statistics

Standard deviation17.843783
Coefficient of variation (CV)0.48586627
Kurtosis-1.22148
Mean36.725709
Median Absolute Deviation (MAD)16
Skewness-0.056497487
Sum42712
Variance318.4006
MonotonicityNot monotonic
2024-04-14T13:54:04.582742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 45
 
3.9%
60 41
 
3.5%
16 35
 
3.0%
58 32
 
2.8%
55 31
 
2.7%
53 30
 
2.6%
57 28
 
2.4%
30 27
 
2.3%
18 24
 
2.1%
48 23
 
2.0%
Other values (63) 847
72.8%
ValueCountFrequency (%)
1 1
 
0.1%
3 2
 
0.2%
4 3
 
0.3%
5 6
 
0.5%
6 13
1.1%
7 11
0.9%
8 18
1.5%
9 18
1.5%
10 17
1.5%
11 19
1.6%
ValueCountFrequency (%)
82 1
 
0.1%
80 1
 
0.1%
73 1
 
0.1%
72 2
 
0.2%
71 3
0.3%
69 4
0.3%
68 3
0.3%
67 4
0.3%
66 6
0.5%
65 1
 
0.1%

Goal
Real number (ℝ)

SKEWED 

Distinct342
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39378.702
Minimum10
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:04.749893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile400
Q11874.835
median7000
Q322636.7
95-th percentile124012.76
Maximum10000000
Range9999990
Interquartile range (IQR)20761.865

Descriptive statistics

Standard deviation311854.37
Coefficient of variation (CV)7.9193663
Kurtosis900.95392
Mean39378.702
Median Absolute Deviation (MAD)6001
Skewness28.638079
Sum45797431
Variance9.7253147 × 1010
MonotonicityNot monotonic
2024-04-14T13:54:04.914042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 63
 
5.4%
10000 58
 
5.0%
1000 45
 
3.9%
15000 39
 
3.4%
25000 33
 
2.8%
20000 32
 
2.8%
30000 29
 
2.5%
3000 25
 
2.1%
50000 24
 
2.1%
1500 22
 
1.9%
Other values (332) 793
68.2%
ValueCountFrequency (%)
10 2
 
0.2%
15.32 1
 
0.1%
56.59 1
 
0.1%
64.76 1
 
0.1%
76.62 1
 
0.1%
100 7
0.6%
113.18 2
 
0.2%
120 1
 
0.1%
143.91 4
0.3%
150 2
 
0.2%
ValueCountFrequency (%)
10000000 1
 
0.1%
2500000 1
 
0.1%
1131835 1
 
0.1%
1000000 1
 
0.1%
905468 1
 
0.1%
747011.1 1
 
0.1%
719547.5 1
 
0.1%
565917.5 1
 
0.1%
500000 3
0.3%
494270 1
 
0.1%

Duration
Real number (ℝ)

Distinct52
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.655202
Minimum4
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:05.064178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile16
Q130
median30
Q335
95-th percentile60
Maximum62
Range58
Interquartile range (IQR)5

Descriptive statistics

Standard deviation11.534306
Coefficient of variation (CV)0.34271986
Kurtosis0.82260077
Mean33.655202
Median Absolute Deviation (MAD)1
Skewness0.96773294
Sum39141
Variance133.04022
MonotonicityNot monotonic
2024-04-14T13:54:05.215315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 570
49.0%
60 116
 
10.0%
45 59
 
5.1%
40 37
 
3.2%
31 36
 
3.1%
35 34
 
2.9%
21 24
 
2.1%
28 23
 
2.0%
15 22
 
1.9%
25 21
 
1.8%
Other values (42) 221
 
19.0%
ValueCountFrequency (%)
4 1
 
0.1%
7 6
 
0.5%
8 2
 
0.2%
9 2
 
0.2%
10 5
 
0.4%
13 1
 
0.1%
14 14
1.2%
15 22
1.9%
16 7
 
0.6%
17 5
 
0.4%
ValueCountFrequency (%)
62 1
 
0.1%
60 116
10.0%
59 3
 
0.3%
58 3
 
0.3%
57 2
 
0.2%
56 1
 
0.1%
55 2
 
0.2%
54 2
 
0.2%
53 2
 
0.2%
51 4
 
0.3%

Pledge Levels
Real number (ℝ)

Distinct35
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1633706
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:05.359446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile17
Maximum89
Range88
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.8528249
Coefficient of variation (CV)0.81704901
Kurtosis35.376501
Mean7.1633706
Median Absolute Deviation (MAD)3
Skewness3.592076
Sum8331
Variance34.255559
MonotonicityNot monotonic
2024-04-14T13:54:05.496570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 146
12.6%
7 104
8.9%
4 103
8.9%
5 100
8.6%
6 99
8.5%
3 94
 
8.1%
2 74
 
6.4%
8 72
 
6.2%
9 70
 
6.0%
10 64
 
5.5%
Other values (25) 237
20.4%
ValueCountFrequency (%)
1 146
12.6%
2 74
6.4%
3 94
8.1%
4 103
8.9%
5 100
8.6%
6 99
8.5%
7 104
8.9%
8 72
6.2%
9 70
6.0%
10 64
5.5%
ValueCountFrequency (%)
89 1
 
0.1%
42 1
 
0.1%
38 1
 
0.1%
32 2
0.2%
31 1
 
0.1%
30 1
 
0.1%
29 1
 
0.1%
28 2
0.2%
27 3
0.3%
26 2
0.2%

Min Pledge Tiers
Real number (ℝ)

SKEWED 

Distinct112
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.526999
Minimum0.7
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:05.646706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1
Q11.13
median5
Q314
95-th percentile100
Maximum10000
Range9999.3
Interquartile range (IQR)12.87

Descriptive statistics

Standard deviation373.74262
Coefficient of variation (CV)9.7007977
Kurtosis553.23533
Mean38.526999
Median Absolute Deviation (MAD)4
Skewness22.664968
Sum44806.9
Variance139683.55
MonotonicityNot monotonic
2024-04-14T13:54:05.940979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 228
19.6%
5 188
16.2%
10 145
12.5%
25 69
 
5.9%
7 42
 
3.6%
4 34
 
2.9%
15 31
 
2.7%
2 26
 
2.2%
20 25
 
2.1%
6 23
 
2.0%
Other values (102) 352
30.3%
ValueCountFrequency (%)
0.7 1
 
0.1%
0.71 1
 
0.1%
0.72 2
 
0.2%
0.73 2
 
0.2%
0.74 2
 
0.2%
0.75 3
 
0.3%
0.76 11
 
0.9%
0.77 5
 
0.4%
0.78 1
 
0.1%
1 228
19.6%
ValueCountFrequency (%)
10000 1
 
0.1%
7237 1
 
0.1%
1690 1
 
0.1%
1450 1
 
0.1%
1000 3
0.3%
658 1
 
0.1%
600 1
 
0.1%
500 5
0.4%
364 1
 
0.1%
362 1
 
0.1%

Max Pledge Tiers
Real number (ℝ)

Distinct379
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1387.3052
Minimum0.76
Maximum25000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:06.095113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.76
5-th percentile11
Q175.5
median310
Q31000
95-th percentile7889.9
Maximum25000
Range24999.24
Interquartile range (IQR)924.5

Descriptive statistics

Standard deviation2539.2134
Coefficient of variation (CV)1.8303207
Kurtosis10.418169
Mean1387.3052
Median Absolute Deviation (MAD)285
Skewness2.8434437
Sum1613435.9
Variance6447604.7
MonotonicityNot monotonic
2024-04-14T13:54:06.256259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 92
 
7.9%
500 72
 
6.2%
100 70
 
6.0%
25 50
 
4.3%
10000 47
 
4.0%
5000 44
 
3.8%
50 29
 
2.5%
250 23
 
2.0%
10 23
 
2.0%
200 22
 
1.9%
Other values (369) 691
59.4%
ValueCountFrequency (%)
0.76 1
 
0.1%
1 12
1.0%
1.13 1
 
0.1%
4 4
 
0.3%
5 8
 
0.7%
6 1
 
0.1%
7 4
 
0.3%
8 2
 
0.2%
10 23
2.0%
11 6
 
0.5%
ValueCountFrequency (%)
25000 1
 
0.1%
10000 47
4.0%
9999 1
 
0.1%
9994 1
 
0.1%
9950 1
 
0.1%
9900 1
 
0.1%
9750 1
 
0.1%
9000 2
 
0.2%
8000 2
 
0.2%
7900 1
 
0.1%

Proj Desc Len
Real number (ℝ)

Distinct1081
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4095.0112
Minimum59
Maximum28293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:06.398389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile655.3
Q11499
median2905
Q35572
95-th percentile11423.6
Maximum28293
Range28234
Interquartile range (IQR)4073

Descriptive statistics

Standard deviation3735.9608
Coefficient of variation (CV)0.91232005
Kurtosis5.6460432
Mean4095.0112
Median Absolute Deviation (MAD)1704
Skewness2.033854
Sum4762498
Variance13957403
MonotonicityNot monotonic
2024-04-14T13:54:06.547524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1878 3
 
0.3%
1180 3
 
0.3%
2397 3
 
0.3%
1119 3
 
0.3%
1449 3
 
0.3%
930 2
 
0.2%
1934 2
 
0.2%
5893 2
 
0.2%
1752 2
 
0.2%
2979 2
 
0.2%
Other values (1071) 1138
97.9%
ValueCountFrequency (%)
59 1
0.1%
67 1
0.1%
110 1
0.1%
129 1
0.1%
191 1
0.1%
225 1
0.1%
240 1
0.1%
244 1
0.1%
248 1
0.1%
252 1
0.1%
ValueCountFrequency (%)
28293 1
0.1%
24482 1
0.1%
23489 1
0.1%
23273 1
0.1%
21308 1
0.1%
21012 1
0.1%
20291 1
0.1%
20035 1
0.1%
20032 1
0.1%
18605 1
0.1%

Images
Real number (ℝ)

ZEROS 

Distinct64
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8710232
Minimum0
Maximum95
Zeros456
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:06.705667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q310
95-th percentile35
Maximum95
Range95
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.270287
Coefficient of variation (CV)1.5589189
Kurtosis8.6959912
Mean7.8710232
Median Absolute Deviation (MAD)3
Skewness2.6146067
Sum9154
Variance150.55994
MonotonicityNot monotonic
2024-04-14T13:54:06.859807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 456
39.2%
3 59
 
5.1%
6 50
 
4.3%
1 48
 
4.1%
5 47
 
4.0%
2 43
 
3.7%
8 39
 
3.4%
4 39
 
3.4%
7 38
 
3.3%
9 31
 
2.7%
Other values (54) 313
26.9%
ValueCountFrequency (%)
0 456
39.2%
1 48
 
4.1%
2 43
 
3.7%
3 59
 
5.1%
4 39
 
3.4%
5 47
 
4.0%
6 50
 
4.3%
7 38
 
3.3%
8 39
 
3.4%
9 31
 
2.7%
ValueCountFrequency (%)
95 1
0.1%
83 1
0.1%
77 1
0.1%
74 1
0.1%
72 1
0.1%
70 1
0.1%
69 1
0.1%
64 1
0.1%
61 1
0.1%
60 1
0.1%

Videos
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8194325
Minimum0
Maximum13
Zeros401
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-14T13:54:06.984920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97740428
Coefficient of variation (CV)1.1927819
Kurtosis44.837251
Mean0.8194325
Median Absolute Deviation (MAD)0
Skewness4.9543186
Sum953
Variance0.95531913
MonotonicityNot monotonic
2024-04-14T13:54:07.113036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 666
57.3%
0 401
34.5%
2 57
 
4.9%
3 21
 
1.8%
4 8
 
0.7%
5 3
 
0.3%
7 2
 
0.2%
6 1
 
0.1%
9 1
 
0.1%
13 1
 
0.1%
Other values (2) 2
 
0.2%
ValueCountFrequency (%)
0 401
34.5%
1 666
57.3%
2 57
 
4.9%
3 21
 
1.8%
4 8
 
0.7%
5 3
 
0.3%
6 1
 
0.1%
7 2
 
0.2%
9 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
13 1
 
0.1%
11 1
 
0.1%
10 1
 
0.1%
9 1
 
0.1%
7 2
 
0.2%
6 1
 
0.1%
5 3
 
0.3%
4 8
 
0.7%
3 21
 
1.8%
2 57
4.9%

Has Video
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
1
762 
0
401 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1163
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 762
65.5%
0 401
34.5%

Length

2024-04-14T13:54:07.240152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:54:07.343246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 762
65.5%
0 401
34.5%

Most occurring characters

ValueCountFrequency (%)
1 762
65.5%
0 401
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 762
65.5%
0 401
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 762
65.5%
0 401
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 762
65.5%
0 401
34.5%

Interactions

2024-04-14T13:53:58.998677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:44.533554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.025908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.296062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.610253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.928449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.300694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.550827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.807968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.139175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.402320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.782582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.112780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:44.658669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.141014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.410165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.727359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.037548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.410793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.662929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.921071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.252278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.645542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.888670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.219878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:44.771772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.247110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.520264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.838460image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.144645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.514888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.769025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.026166image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.357374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.751638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.991763image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.329977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:44.889878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.359210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.632366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.956567image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.253744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.623987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.881135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.140269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.469475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.860737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.099861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.444080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.040014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.472313image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.753476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.073673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.366847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.735088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.991227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.273390image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.583579image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.973840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.208960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.546173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.158121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.577409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.862575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.181772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.466938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.840183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.095322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.390496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.689675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.077933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.309051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.649267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.267221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.679502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.970672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.286867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.567037image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.941274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.193411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.491588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.791767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.180027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.409141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.747355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.370314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.777591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.073767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.388959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.794243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.038362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.286494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.588676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.890857image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.279117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.507230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.850449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.477411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.882686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.182866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.497057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.895326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.140455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.411608image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.689768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-04-14T13:53:58.606320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:59.952541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.584508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:46.985780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.289963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.606156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:50.998419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.243548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.510698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.791860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.097044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.482300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.705410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:54:00.055636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.816719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.089875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.397060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.716257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.099511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.349645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.612791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:54.895955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.199137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.579388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.803499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:54:00.156726image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:45.919813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:47.188964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:48.501154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:49.819350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:51.196599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:52.444732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:53.706877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:55.035081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:56.295224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:57.677478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-04-14T13:53:58.897585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-04-14T13:54:00.326881image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T13:54:00.623150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FundedURLTitleYearMonthTypeHas FBBacked ProjectsPrevious ProjectsCreator Desc LenTitle LenGoalDurationPledge LevelsMin Pledge TiersMax Pledge TiersProj Desc LenImagesVideosHas Video
0nohttps://www.kickstarter.com/projects/mischaa/pixelstart-choose-your-own-pixelsPixelstart: Choose Your Own Pixels (Canceled)2016AprArt1112125572829.595371.14171.02001211
1nohttps://www.kickstarter.com/projects/1055874454/smart-shop-iconsSmart shop Icons (Canceled)2016AprArt1001112728295.875131.1446.02508000
2nohttps://www.kickstarter.com/projects/minimalprints/minimal-haus-prints-digital-prints-for-diy-wall-arMinimal Haus Prints: Digital Prints for DIY Wall ART2016AprArt04029452766.253081.51755.02325111
3nohttps://www.kickstarter.com/projects/797661619/neon-altering-the-alternativeNeoN: Altering the Alternative (Canceled)2016MarArt000179411439.102457.00141.037361311
4nohttps://www.kickstarter.com/projects/1983693599/nintendo-nes-8bit-retro-canvasNintendo NES 8bit retro canvas (Canceled)2016MarArt00051411000.003025.0020.0636000
5nohttps://www.kickstarter.com/projects/463662725/day-and-night-cardsDay and Night Cards (Canceled)2016MarArt00016230800.0028110.0010.01199000
6nohttps://www.kickstarter.com/projects/jforwoodart/fund-an-art-show-and-beyond-for-jeff-forwoodFund an Art Show and Beyond For Jeff Forwood (Canceled)2016FebArt120110552000.005095.00500.023841100
7nohttps://www.kickstarter.com/projects/109608826/trump-that-t-are-hilarious-creative-and-beautifulTrump that T are hilarious\t creative\t and beautiful shirts2016MarArt11012581000.003031.0030.01603111
8yeshttps://www.kickstarter.com/projects/cowgirlem/once-upon-a-pictureOnce Upon a Picture2016AprArt140285191079.3230167.00157.02397911
9yeshttps://www.kickstarter.com/projects/10642514/under-the-hood-an-art-book-by-sean-gordon-murphyUnder the Hood: An Art book by Sean Gordon Murphy2016AprArt1128554910000.0030195.008000.013071311
FundedURLTitleYearMonthTypeHas FBBacked ProjectsPrevious ProjectsCreator Desc LenTitle LenGoalDurationPledge LevelsMin Pledge TiersMax Pledge TiersProj Desc LenImagesVideosHas Video
1153nohttps://www.kickstarter.com/projects/824780450/reallysendReallySend2016MarSoftware1306011047537.073076.002777.074072311
1154nohttps://www.kickstarter.com/projects/dharlaa/dharlaacom-answers-for-everyoneDharlaa.com - Answers for everyone2016MarSoftware1004983483500.0030450.0010000.020032011
1155nohttps://www.kickstarter.com/projects/853681317/solidprojection-solidworks-to-unity3d-with-substanSolidProjection SolidWorks to Unity3D with Substance Painter2016MarSoftware00048860766.25305151.00151.01395011
1156nohttps://www.kickstarter.com/projects/362792417/findclone-mobile-appFindClone mobile app2016MarSoftware100552028295.874065.00546.043621111
1157nohttps://www.kickstarter.com/projects/1471372310/homesnipscomHomeSnips.com2016FebSoftware000961310000.00601100.00100.02547000
1158nohttps://www.kickstarter.com/projects/2045809865/qualia-osQualia OS2016MarSoftware100539143909.5030214.00143.0508000
1159nohttps://www.kickstarter.com/projects/1349408487/team-fastpadTeam FastPad2016MarSoftware010502129950.003055.001800.05958311
1160nohttps://www.kickstarter.com/projects/ubergeekzone/foodme-rideshare-restaurant-deliveryFoodMe - Rideshare Restaurant Delivery2016MarSoftware0004913820000.0030215.0025.01165000
1161nohttps://www.kickstarter.com/projects/1233788194/free-coding-lesson-in-sydneyFree Coding Lesson in Sydney2016FebSoftware020513283831.256020.71708.0852011
1162nohttps://www.kickstarter.com/projects/adamhenson/jillion-an-open-source-javascript-framework-for-htJillion: An Open-Source JavaScript Framework for HTTP/22016MarSoftware1042535525000.0030141.005000.03738000